1. From Intelligent Control to Cognitive Control: A Perspective from Cognitive Robot Engineering Point of View
Kaz Kawamura
Center for Intelligent Systems
Vanderbilt University

2. Background
Our group have been working on a robotic system called ISAC (Intelligent Soft Arm Control) since late 1980s (as an industry-sponsored project.)
ISAC was initially developed as a robotic aid system using vision, voice and haptic-based adaptive control.

3. Background
Our group have been working on a robotic system called ISAC (Intelligent Soft Arm Control) since late 1980s (as an industry-sponsored project.)
Long-term goal was to develop an assembly “horon” (i.e. a cognitive co-worker) for intelligent manufacturing systems.

4. Background
Our group have been working on a robotic system called ISAC (Intelligent Soft Arm Control) for the last fifteen years.
ISAC was initially developed as a robotic aid system using vision, voice and haptic-based adaptive control.
Over the years, we gradually added hardware components and adopted a modular software development approach, i.e. multi-agent-based “hybrid architecture ( more like one Troy Kelly mentioned)”.

5. Background
Our group have been working on a humanoid robotic system called ISAC (Intelligent Soft Arm Control) for the last ten years.
ISAC was initially developed as a robotic aid system using haptic-based adaptive control.
In the last several years, we are adding computational modules to incorporate some of cognitive psychology (i.e. an central executive (A. Baddeley)) and neuroscience (i.e. an adaptive working memory (David Noelle))-based models to realize “cognitive control “ functionalities to ISAC.

6. Are these robots intelligent, cognitive or neither?
COG, MIT (Is COG the “Father of cognitive robots”?)
ISAC, Vanderbilt
Robonaut, NASA (Is it a vision of an ultimate cognitive robot?)
Many others shown by the workshop participants (Rolf, Olaf, Owen, etc.)

7. Hypothesis
Artificial cognitive agents must share key features and “neurobiological and cognitive principles” (Jeff Krichmar) with humans if they are to become effective partners and coworkers in the human society.

8. Process of Cognitive (or Executive) Control
Human (and some animal) brain is known to process a variety of stimuli in parallel and choose appropriate action under conflicting goals. (Figure below was taken from: P. Haikonen, The Cognitive Approach to Conscious Machine, 2003)

9. Human Cognitive Control Functions
Ability of the brain to execute task and resolve conflicts
Focus on task context and ignore distraction
Involves action selection and control where reactive sensorimotor-based action execution falls short of task demands. Example: Stroop test
Cognitive Control
Modified from: Miller, E.K., Cognitive Control: Understanding the brain’s executive, in Fundamentals of the Brain and Mind, Lecture 8, June 11-13, 2003, MIT.
Vanderbilt University

10. Stroop Test (Experiment) (J. R
Stroop Test (Experiment) (J.R. Stroop, Studies of interference in serial verbal reaction, J. of Experimental Psychology, 1935)
One classic experiment used to demonstrate and test the ability of human cognitive control to inhibit competing responses.
The participants are asked to do the following task:
In this task, a lists of words is presented on colored papers.
“name the color” of the written text (red, blue, green, etc.) as fast as possible.
Experiments found that people are more easily distracted by words than color, i.e. they had a harder time to name the color different from the text written on it.
Cognitive Control

11. NASA-JSC Robonaut Demo: “An Ultimate Cognitive Robot?”

12. Key Features of Cognitive Robots (A Partial/Unproven/Controvertial List )
Ability to perceive the world in a similar way to humans (or better) (e.g., “active perception”, Olaf Sporns, “ecological approach to perception”, JJ Gibson)
Ability to develop cognition through sensorymotor coordination (e.g., “morphological computation”, Rolf Pfeifer)
Ability to communicate with humans using natural language and mental models (robust HRI such as overcoming the frame of reference problem, Alan Schultz)
Ability to have a sense of self awareness (internal model and machine consciousness, Igor Alexander, Owen Holland vs. Kevin O”Reagan)
Ability to use attention and emotion to control behaviors (cognitive control)
NASA’s Robonaut
Vanderbilt University

13. Concept of a Cognitive Robotic System
Adapted from a DARPA ITPO Program web site, 2003.

14. Working Definition
Cognitive Control for robots is the attention- and emotion-based robust sensory-motor intelligence to execute the task in hand or switch tasks under conflicting goals.

15. Working Memory System STM LTM Action Stimuli Atomic Agents Head Agent
Arm Agents
Action
Actuators
Human
Agent
Hand Agents
Legend
SES= Sensory EgoSphere
PM= Procedural Memory
SM=Semantic Memory
EM=Episodic Memory
CEA=Central Executive Agent
Self Agent
Stimuli
Perception
Encodings
Sensors
CEA
Working
Memory
System
Completed
SES
Currently being implemented
Attention
Network
STM
Behavior 1 …
Behavior N … SM EM PM
LTM
Behaviors

16. Cognitive Control on ISAC
Ability to use attention and emotion to control behaviors (i.e., cognitive control) is being implemented using the Sensory EgoSphere, the Attention Network, Emotion, the Working Memory System, the Central Executive Agent, and others.
Vanderbilt University

17. Current Work
Current Work is aimed at testing how modules involved in cognitive control work together as a system:
1. Working Memory System Training
[Poster Presentation by Stephen Gordon]
2. Situation-based Action Selection

18. 1. Control Structure used during working memory system training

19. Experiment I: Working Memory Training for a Percept-Action Task
ISAC is trained to recognize specific objects
i.e., several colored bean bags.
2. ISAC is taught a small set of motion behaviors
i.e., reach, wave, handshake.
3. Bean bags are rearranged.
4. ISAC is asked to “reach to the bean bag”
(color is not specified).
Vanderbilt University

20. Experiment I
ISAC is trained to recognize specific objects ,i.e., several colored bean bags.
ISAC is taught a small set of motion behaviors ,i.e., reach, wave, handshake.
Bean bags are rearranged.
ISAC is asked to “reach to the bean bag” (color is not specified).
ISAC will attempt to load the relevant “chunks” into WMS for appropriate:
action to take (reach, wave, etc.)
percept to act upon.
Over time, ISAC should learn
which “chunk” (i.e., a percept-behavior combination) is the most appropriate to choose
Vanderbilt University

21. Working Memory System Training

22. Experiment I (cont’d) Sample configuration for reaching
Second sample configuration
(top view)
Sample configuration for reaching
(top view)
Vanderbilt University

23. Experiment I - Video
Vanderbilt University

24. Learning Results for Reaching Action

25. Experiment II: Situation-Based Task Switching (Under Investigation)
Vanderbilt University

26. Experiment II
A simulation experiment to test key system components for cognitive control using CEA, attention network, and emotion
A simple situation-based task switching using the Focus of Attention (next slide) is being

27. Situation-based Action Selection (Under investigation)

28. Experiment II - Video
Vanderbilt University

29. Simulation Results

30. What have we learned so far?
Effectiveness of using a computational neuroscience-based working memory model for perception-behavior learning on a robot (proof of concept)
Computational time of the WM software library is expected to grow exponentially as the robot accumulates experience (classical AI problem) (effective use of episodic memory?)
WM model does not seem effective for task switching
Needs a better mechanism than a FOA-based situational change for task switching (=> dynamic modeling of situations)

31. Thank you!
For further information, please visit our website at:
Vanderbilt University

32. Background
Our group have been working on a humanoid robotic system called ISAC (Intelligent Soft Arm Control) for the last ten years.
ISAC was initially developed as a robotic aid system using sensor-based intelligent control.

33. Human Agent

34. Self Agent
The key agent in our cognitive architecture is the Self Agent.
Minsky calles it the “Self Model” in his forthcoming book, The Emotion Machine.
Actually he uses the term “Self Models” which include both the Self Agent and the Human Agent in our architecture.

35. Self Agent STM LTM Human Agent Atomic Agents SES Working Memory System
Description
Agent
Intention Agent
Atomic
Agents
Anomaly Detection
Agent
Activator Agent
Mental Experiment
Agent
Emotion Agent
Legend
SES= Sensory EgoSphere
PM= Procedural Memory
SM=Semantic Memory
EM=Episodic Memory
CEA=Central Executive Agent
First-order
Response Agent
Central Executive
Agent
Completed
Currently being implemented
Not yet implemented
SES
Working
Memory
System SM PM
Behavior 1 …
Behavior N EM
STM …
LTM
Behaviors

36. Central Executive Agent (CEA): Robotic Frontal Lobes responsible for cognitive control functions
Inspired by the “central executive” from Baddeley’s working memory model (Baddeley, 1986)
Functions of CEA include
Obtaining task sequence for task execution
Decision making
Action execution
Task monitoring
Decision
Making
Task
Execution
Task-related
Percepts
Response
To Percepts
From
Initial
Knowledge
Environment
Task execution
sequences
Candidate Task
Execution Sequences
Selected Task
Action
Feedback
A. Baddeley, Working Memory, 11, Oxford Psychology Series, Oxford: Clarendon Press, 1986.
Vanderbilt University

37. Questions
1. How could cognitive control be implemented in robotics? (model or no model?)
2. How does one know when a robot becomes a cognitive robot?
Vanderbilt University